Motion correction has potential to affect diagnosis or patient care.
There was better quantification and detection of PET-avid lesions with the addition of a motion correction algorithm, according to a study published in the Journal of Nuclear Medicine.
Researchers from the United Kingdom sought to determine the ability of their joint PET/MRI-based predictive motion model to correct respiratory motion in PET, demonstrating that the algorithm could improve lesion detectability and quantitation and reduce image artifacts.
Forty-two clinical PET/MRI patient datasets containing 162 PET-avid lesions were included in the study. The researchers used multiple tracers and multiple organ locations to apply their motion correction method to the datasets. Quantitative changes were calculated using SUV changes in avid lesions. Lesion detectability changes were explored with a study; two radiologists identified lesions in uncorrected and motion-corrected images and provided confidence scores.
The results showed mean increases of 12.4% for SUVpeak and 17.6% for SUVmax after motion correction were found. In the detectability study, confidence scores for detecting avid lesions increased, with a rise in mean score from 2.67 to 3.01 (of 4) after motion correction and a rise in detection rate from 74% to 84%. Of 162 confirmed lesions, 49 showed an increase in all three metrics-SUVpeak, SUVmax, and combined reader confidence score-whereas only two lesions showed a decrease.
The researchers also presented clinical case studies that demonstrated the effect of respiratory motion correction of PET data on patient management, with increased numbers of detected lesions, improved lesion sharpness and localization, and reduced attenuation-based artifacts.
They concluded that there were significant improvements in quantification and detection of PET-avid lesions, with specific case study examples showing where motion correction has the potential to affect diagnosis or patient care.
Current Insights and Emerging Roles for Contrast-Enhanced Mammography
May 10th 2024In a recent lecture at the 2024 ARRS Annual Meeting, Jordana Phillips, MD, discussed the role of contrast-enhanced mammography in staging breast cancer, evaluating response to neoadjuvant chemotherapy and recalls from screening.
MRI-Based Deep Learning Algorithm Shows Comparable Detection of csPCa to Radiologists
May 8th 2024In a study involving over 1,000 visible prostate lesions on biparametric MRI, a deep learning algorithm detected 96 percent of clinically significant prostate cancer (csPCa) in comparison to a 98 percent detection rate for an expert genitourinary radiologist.
Breast MRI and Dense Breasts: A Closer Look at Early Findings from a New Prospective Trial
May 2nd 2024Supplemental breast MRI had a cancer detection rate (CDR) of 20/1000 and a positive predictive value (PPV) of 50 percent, according to preliminary findings from a prospective trial involving women with heterogeneously or very dense breasts.